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This chapter examines the impact of sectoral indebtedness on GDP in Czechia, initially a low-indebted small open economy in which debt dynamics are becoming a major concern. The impact of household debt, non-financial corporation debt and public debt is analysed with the use of local projections based on instrumental variable estimations. The results show a more pronounced influence of household debt compared to non-financial corporation and government debt. Initially, increasing household debt stimulates short-run economic activity, but in the medium run, it limits household consumption and negatively affects output. This negative impact gradually turns into a positive effect in the long run. Non-financial corporation debt has a negative short- to medium-run impact but can have a small positive effect in the long run due to the prevalence of tradable industries. Public debt initially has a short-run negative impact, but then gradually becomes positive. Overall, the findings have implications for macroeconomic policies and the importance of monitoring financial stability.

Since 2008’s financial crisis, attention toward supply chain finance (SCF) has increased. However, most research investigates SCF considering single supply chain (SC) stages or buyer–supplier dyads and focuses on a single SCF solution. It is important to see how different solutions are adopted at different SC stages, by actors with different financing needs. This study aims to analyze SCF at different SC stages, to understand why different solutions are implemented at different SC stages and the contingency factors (regulation, SC stage, product category and size) influencing their adoption.

The paper is based on multiple exploratory case studies in the Italian agri-food industry, considering firms distributed at different SC stages and adopting multiple SCF solutions. The paper exploits a contingent approach (Sousa and Voss, 2008) to analyze how contingent factors influence SCF adoption at different SC stages.

Findings explain how and why different SC stages (producer, cooperative, processor and retailer) implement different SCF solutions (reverse factoring, dynamic discounting, inventory finance and Minibond), describing contingency variables’ impact on their adoption.

To the best of the authors’ knowledge, the research is original in its description of SCF at different SC stages, considering different SC actors’ drivers and barriers, and questioning the importance of a coordinated approach in SCF adoption along an entire SC. Moreover, the paper adopts a contingent approach, contributing to SCF research, seldomly based on theoretical lenses.

Monetary policy has major impacts on macroeconomic indicators of the country. Accordingly, uncertainty regarding monetary policy shifts can cause challenges and risks for businesses, financial markets and investors. Thus, the purpose of this study is to investigate how real estate market volatility responds to monetary policy uncertainty.

The GARCH-MIDAS model is applied in this study to investigate the nexus between monetary policy uncertainty and real estate market volatility. This model was fundamentally instituted to accommodate low-frequency variables.

The results of this study reveal that increased monetary policy uncertainty highly affects the volatility in real estate market during the peak period of COVID-19 as compared to full sample period and COVID-19 recovery period; hence, a significant decline is evident in real estate market volatility during crisis.

This study is particularly focused on peak and recovery period of COVID-19 considering the geographical region of Greece, Japan and the USA. This study provides a complete perspective on the nexus between monetary policy uncertainty and real estate markets volatility in three distinct economic views.

Our purpose is the examination of the effects of fiscal policy on private lending for the Eurozone countries. The emphasis is on the identification of the time path of government spending and bank lending.

Fiscal policy is a main factor of macroeconomic stability for the euro area economy. This paper, investigates the impact of government spending on bank lending. For this reason, we present a dynamic theoretical model with a perfectly competitive banking sector, estimated using panel cointegration for the Eurozone countries from 2000Q1 to 2022Q2.

Our findings highlight that, in the long run, consistent management of government spending can have a beneficial multiplicative impact on bank lending for housing and business reasons. This finding is stronger in magnitude for business versus housing lending. The high level of homogeneity of our results across Eurozone countries has positive implications for a common fiscal policy in the future. Finally, authorities should know that policy adjustments are quicker in housing lending when compared to business lending.

In this paper, we contribute to the existing literature, concentrating on the investigation of any existence of long-run and short-run relationships between government spending and bank lending. Additionally, our analysis allows one to investigate the contribution of each Eurozone member state in the short-run and long-run model’s dynamics, providing significant outcomes for the implementation of economic policy and the need for fiscal discipline in the Eurozone.

Inflation expectations are an important part of the transmission mechanism of the inflation targeting regime. As such, central bankers must study the inflation expectations of economic agents to anchor them close to the level of the inflation target. However, economic agents are affected by the past and current macroeconomic situation when they form their expectations concerning future inflation. Using survey data on inflation expectations in Czechia, we investigate the macroeconomic determinants of Czech analysts' and managers' inflation expectations. We find that both actual and past inflation have a substantial impact on inflation expectations of the agents surveyed. We also identify backward-looking behaviour among these agents: persistence in inflation expectations of up to two quarters was detected. Moreover, financial analysts formed inflation expectations more in line with economic theory, while company managers evinced expectations similar to those of consumers.

Fintech plays a prominent role in augmenting the financial inclusion of the population and increasing the money supply, which calls for the intervention of monetary policy. This article is an attempt to examine the relationship between the financial inclusion, fintech and monetary policy effectiveness of the Indian economy, within the framework of wealth creation and transmission mechanism through the cost of capital.

On the quarterly data retrieved from multiple sources, autoregressive distributed lagged regression is used to examine the relationship between different variables as explained in four set models; after which the Toda–Yamamoto causality test is employed to capture the direction of the relationship.

The study finds a positive relationship between financial inclusion, fintech and inflation taken as a proxy for Monetary Policy Effectiveness (MPE) in the short as well as in the long run. However, the relationship between fintech and inflation is negative once the cost of capital is included in the models. The causality test exhibits the uni-directional causality from fintech to MPE and MPE to financial inclusion. Bi-directional causality exists between wealth and MPE. Similarly, bank rate and interbank rate are bound by bi-directional causality.

Being financially included facilitates ease and boosts public access to more financial services and credit, leading to increased demand and hence inflation. Hence government and regulators need to take mindful measures to enhance the fintech development and financial inclusion to make the monetary policy effective.

As per the author's best knowledge, this is the first study to examine the relationship between fintech, financial inclusion and monetary policy effectiveness in the context of the Indian economy.

Throughout the 21st century, US households have experienced unprecedented levels of leverage. This dynamic has been exacerbated by income shortfalls during the COVID-19 crisis. Leveraging and deleveraging decisions affect household consumption. This study investigates the effect of the dynamics of household leverage and consumption on the stock market.

The authors explore the relation between household leverage and consumption in the context of the consumption capital asset pricing model (CCAPM). The authors test the model's implication that leverage has a negative risk premium by transforming the asset pricing restriction into an unconditional linear factor model and estimate the model using the general method of moments procedure. The authors run time-series regressions to estimate individual stocks' exposures to leverage, and cross-sectional regressions to investigate the leverage risk premium.

The authors show that shocks to household debt have strong and lasting effects on consumption growth. The authors extend the CCAPM to accommodate this effect and find, using various test assets, a negative risk premium associated with household deleveraging. Looking at individual stocks the authors show that the deleveraging risk premium is not explained by well-known risk factors.

This paper contributes to the literature on the role of leverage in economics and finance by establishing a relation between household leverage and spending decisions. The authors provide novel evidence that households' leveraging and deleveraging decisions can be a fundamental and influential force in determining asset prices. Further, this paper argues that household leverage might explain the small, persistent, and predictable component in consumption growth hypothesised in the long-run risk asset pricing literature.

The process of predicting construction costs and forecasting price fluctuations is a significant and challenging undertaking for project managers. This study aims to develop a construction cost index (CCI) for Jordan’s construction industry using fuzzy analytic hierarchy process (FAHP) and predict future CCI values using traditional and machine learning (ML) techniques.

The most influential cost items were selected by conducting a literature review and confirmatory expert interviews. The cost items’ weights were calculated using FAHP to develop the CCI formula.

The results showed that the random forest model had the lowest mean absolute percentage error (MAPE) of 1.09%, followed by Extreme Gradient Boosting and K-nearest neighbours with MAPEs of 1.41% and 1.46%, respectively.

The novelty of this study lies within the use of FAHP to address the ambiguity of the impact of various cost items on CCI. The developed CCI equation and ML models are expected to significantly benefit construction managers, investors and policymakers in making informed decisions by enhancing their understanding of cost trends in the construction industry.

This paper aims to examine the effects of corruption control on domestic investment in the BRICS and CIVETS of emerging economies. This paper’s primary goal is to investigate how corruption has impacted domestic private investment in BRICS and CIVETS, empirically evaluate that impact and offer appropriate policy recommendations.

This paper uses secondary panel data from the World Bank spanning the period 2000–2020. The data covered the BRICS and CIVETS countries between 2000 and 2020. This study used gross domestic product (GDP) per capita growth, broad money as a percentage of GDP, real interest rate and corruption index as independent variables and domestic investment as a percentage of GDP as a dependent variable.

The significant results are presented using the panel, autoregressive distributed lag pooled mean group estimator. Growth in the per-capita GDP, money supply and the suppression of corruption all have long-term, positive and significant benefits on domestic investment. Comparatively, the real interest rate has a significant negative influence on investment, indicating that it may be necessary and beneficial to adopt anti-corruption measures to promote domestic investment. However, the country-specific analysis reveals that the long-term effects of corruption on investment tend to vary across countries, indicating that each country needs to research the issue of corruption independently. Finally, ensuring optimal levels of money supply and interest rates leaded by further control of corruption is necessary for strengthening the investment environment.

This study suggests several practical implications. For example, legislators and policymakers should pay more attention to anti-corruption policies. Central banks should put more effort into controlling the interest rate.